Space-time coded transmissions within a wireless communication network

ABSTRACT

Techniques are described for space-time block coding for single-carrier block transmissions over frequency selective multipath fading channels. Techniques are described that achieve a maximum diversity of order N t N r  (L+1) in rich scattering environments, where N t  (N r ) is the number of transmit (receive) antennas, and L is the order of the finite impulse response (FIR) channels. The techniques may include parsing a stream of information-bearing symbols to form blocks of K symbols, preceding the symbols to form blocks having J symbols, and collecting consecutive N s  blocks. The techniques may further include applying a permutation matrix to the N s  blocks, generating a space-time block coded matrix having N t  rows that are communicated through a wireless communication medium. The receiver complexity is comparable to single antenna transmissions, and the exact Viterbi&#39;s algorithm can be applied for maximum-likelihood (ML) optimal decoding.

[0001] This application claims priority from U.S. Provisional Application Serial No. 60/293,476, filed May 25, 2001, the entire content of which is incorporated herein by reference.

[0002] This invention was made with government support under ECS-9979443 awarded by the National Science Foundation. The government has certain rights in the invention.

TECHNICAL FIELD

[0003] The invention relates to communication systems and, more particularly, multiple-antennae transmitters and receivers for use in wireless communication systems.

BACKGROUND

[0004] Space-time (ST) coding using multiple transmit-antennae has been recognized as an attractive means of achieving high data rate transmissions with diversity and coding gains in wireless applications. However, ST codes are typically designed for frequency flat channels. Future broadband wireless systems will likely communicate symbols with duration smaller than the channel delay spread, which gives rise to frequency selective propagation effects. When targeting broadband wireless applications, it is important to design ST codes in the presence of frequency selective multipath channels. Unlike flat fading channels, optimal design of ST codes for dispersive multipath channels is complex because signals from different antennas are mixed not only in space, but also in time. In order to maintain decoding simplicity and take advantage of existing ST coding designs for flat fading channels, most conventional techniques have pursued two-step approaches. In particular, the techniques mitigate intersymbol interference (ISI) by converting frequency selective fading channels to flat fading channels, and then design ST coders and decoders for the resulting flat fading channels. One approach to ISI mitigation has been to employ a relatively complex multiple-input multiple-output equalizer (MIMO-EQ) at the receiver to turn FIR channels into temporal ISI-free ones.

[0005] Another approach, with lower receiver complexity, is to employ orthogonal frequency division multiplexing (OFDM), which converts frequency selective multipath channels into a set of flat fading subchannels through inverse Fast Fourier Transform (FFT) and cyclic prefix (CP) insertion at the transmitter, together with CP removal and FFT processing at the receiver. On the flat fading OFDM subchannels, many techniques have applied ST coding for transmissions over frequency-selective channels. Some of these assume channel knowledge, while others require no channel knowledge at the transmitter.

[0006] Although using ST codes designed for flat fading channels can at least achieve full multi-antenna diversity, the potential diversity gains embedded in multipath propagation have not been addressed thoroughly. OFDM based systems are able to achieve both multi-antenna and multipath diversity gains of order equal to the product of the number of transmit-antennas, the number of receive-antennas, and the number of FIR channel taps. However, code designs that guarantee full exploitation of the embedded diversity have not been explored. A simple design achieves full diversity, but it is essentially a repeated transmission, which decreases the transmission rate considerably. On the other hand, for single antenna transmissions, it has been shown that a diversity order equal to the number of FIR taps is achievable when OFDM transmissions are linearly precoded across subcarriers. An inherent limitation of multicarrier (OFDM) based ST transmissions is a non-constant modulus, which necessitates power amplifier back-off, and thus reduces power efficiency. In addition, multi-carrier schemes are more sensitive to carrier frequency offsets relative to their single-carrier counterparts.

SUMMARY

[0007] In general, the invention is directed to space-time block coding techniques for single carrier block transmissions in the presence of frequency-selective fading channels. Furthermore, in accordance with the techniques, a maximum diversity up to order N_(t)N_(r) (L+1) can be achieved in a rich scattering environment, where N_(t) is the number of transmit antennas, N_(r) is the number of receive antennas, and (L+1) is the number of taps corresponding to each FIR channel. The techniques enable simple linear processing to collect full antenna diversity, and incur receiver complexity that is comparable to single antenna transmissions. Notably, the transmissions enable exact application of Viterbi's algorithm for maximum-likelihood (ML) optimal decoding, in addition to various reduced-complexity sub-optimal equalization alternatives. When the transmissions are combined with channel coding, they facilitate application of iterative (turbo) equalizers. Simulation results demonstrate that joint exploitation of space-multipath diversity leads to significantly improved performance in the presence of frequency selective multipath channels.

[0008] In one embodiment, a method may comprise applying a permutation matrix to blocks of symbols of an outbound data stream, and generating transmission signals from the permutated blocks of symbols. The method may further comprise communicating the transmission signals through a wireless communication medium.

[0009] In another embodiment, a method may comprise parsing a stream of information-bearing symbols to form blocks of K symbols, preceding the symbols to form blocks having J symbols, and collecting consecutive N_(s) blocks. The method may further comprise applying a permutation matrix to the N_(s) blocks, generating a space-time block coded matrix having N_(t) rows, each row containing N_(d)*J symbols, generating N_(t) transmission signals from the symbols of the N_(t) rows, and communicating the N_(t) transmission signals through a wireless communication medium.

[0010] In another embodiment, a transmitting device may comprise an encoder to apply a permutation matrix to blocks of information bearing symbols and to generate a space-time block coded matrix of the permutated blocks of symbols. The transmitting device further comprises a plurality of pulse shaping units to generate a plurality of transmission signals from the symbols of the space-time block coded matrix, and a plurality of antennae to communicate the transmission signals through a wireless communication medium.

[0011] The details of one or more embodiments of the invention are set forth in the accompanying drawings and the description below. Other features, objects, and advantages of the invention will be apparent from the description and drawings, and from the claims.

BRIEF DESCRIPTION OF DRAWINGS

[0012]FIG. 1 is a block diagram illustrating a wireless communication system in which a transmitter communicates with a receiver through a wireless channel using space-time coded transmissions.

[0013] FIGS. 2-3 are timing diagrams illustrating the transmitted sequences from the antennas of the transmitter of FIG. 1.

[0014]FIG. 4 is another example transmission format for the transmitter of FIG. 1.

[0015]FIG. 5 is an example communication system using channel coding with the space-time coded transmission techniques in accordance with the principles of the invention.

[0016] FIGS. 6-9 are graphs illustrating simulated performance results for systems with two transmit and one receive antenna.

DETAILED DESCRIPTION

[0017] The Detailed Description is organized as follows: Section I deals with the special case in which a system includes of a receiver having a single antenna, and transmitter having two transmit antennas. Section II details the equalization and decoding designs. Section III generalizes the proposed schemes to multiple transmit- and receive- antennas. Simulation results are presented in Section IV.

[0018] Throughout the Detailed Description, bold upper letters denote matrices, bold lower letters stand for column vectors; ()*, ()^(T) and ()^(H) denote conjugate, transpose, and Hermitian transpose, respectively; Et{} for expectation, tr{} for the trace of a matrix, ∥∥ for the Euclidean norm of a vector; I_(K) denotes the identity matrix of size K, 0_(M×N) (1_(M×N) denotes an all-zero (all-one) matrix with size M×N,and F) _(N)denotes an NXN FFT matrix with the (p+1; q+1)st entry of:

(1/{square root}{square root over (N))}exp (−j2πpq/N), ∀p,q∈[0, N−1[;

[0019] diag(x) stands for a diagonal matrix with x on its diagonal. []_(p) denotes the (p+1)st entry of a vector, and []_(p,q) denotes the (p+1; q+1)st entry of a matrix.

[0020] I. Single Carrier Block Transmissions

[0021]FIG. 1 is a block diagram illustrating a wireless communication system 2 in which a transmitter 4 communicates with a receiver 6 through a wireless communication channel 8. In particular, FIG. 1 illustrates the discrete-time equivalent baseband model in which transmitter 4 transmits a data with two transmit antennas (N_(t)=2), and receiver 6 receives data with a single receive antenna ( N_(r)−1). Transmitter 4 includes a precoder 11, an encoder 12, two pulse shaping units 13 for generating transmission signals, and two transmission antennae 14.

[0022] The information-bearing data symbols d(n) belonging to an alphabet A are first parsed to K×1 blocks d(i):=[d(iK); . . . ; d(iK+K−1)]^(T), where the serial index n is related to the block index i by: n=iK+k; k∈[0;K−1]. The blocks d(i) are precoded by a J×K matrix Θ (with entries in the complex field) to yield J×1 symbol blocks: s(i):=Θd(i). The linear precoding by Θ can be either non-redundant with J=K, or, redundant when J>K. The ST encoder takes as input two consecutive blocks s(2 i) and s(2 i+1) to output the following 2J×2 space-time block coded matrix: $\begin{matrix} {{\begin{bmatrix} {{\overset{\_}{s}}_{1}\left( {2i} \right)} & {{\overset{\_}{s}}_{1}\left( {{2i} + 1} \right)} \\ {{\overset{\_}{s}}_{2}\left( {2i} \right)} & {{\overset{\_}{s}}_{2}\left( {{2i} + 1} \right)} \end{bmatrix}:={\begin{bmatrix} {s\left( {2i} \right)} & {{- P}\quad {s^{*}\left( {{2i} + 1} \right)}} \\ {s\left( {{2i} + 1} \right)} & {P\quad {s^{*}\left( {2i} \right)}} \end{bmatrix}\begin{matrix} \left. \rightarrow\quad {time} \right. \\ {\quad \left. \downarrow\quad {space} \right.} \end{matrix}}},} & (1) \end{matrix}$

[0023] where P is a permutation matrix that is drawn from a set of permutation matrices {P_(J) ^((n))}_(N=0) ^(J−1), with J denoting the dimensionality J×J. Each performs a reverse cyclic shift (that depends on n) when applied to a J×1 vector a:=[a(0); a(1); . . . ; a(J−1)]^(T). Specifically, [P_(J) ^((n))a]_(p)=a((J−p+n) mod J). Two special cases are P_(J) ⁽⁰⁾ and P_(J) ⁽¹⁾. The output of P_(J) ⁽⁰⁾a =[a(J −1); a(J−2); . . . ; a(0)]^(T) performs time-reversal of a, while P_(j) ⁽¹⁾ a=[a(0); a(J−1); a(J−2); . . . ;a(1)]^(T)=F_(J) ⁽⁻¹⁾F_(J) ^((H))=F_(J) ^((H))F_(J) ^((H)) a corresponds to taking the J-point IFFT twice on the vector a. This double IFFT operation in the ST coded matrix is in fact a special case of a Z-transform approach originally proposed in Z. Liu and G. B. Giannakis, “Space-time coding with transmit antennas for multiple access regardless of frequency-selective multi-path,” in Proc. of Sensor Array and Multichannel Signal Processing Workshop, Boston, Mass., March 2000, pp. 178-182., with the Z-domain points chosen to be equally spaced on the unit circle: $\left\{ ^{j\frac{2\pi}{J}n} \right\}_{n = 0}^{J - 1}.$

[0024] The techniques herein allow for any P from the set {P_(J)^((n))}_(n = 0)^(J − 1).

[0025] At each block transmission time interval i, the blocks s₁(i) and s₂(i) are forwarded to the first and the second antennae of transmitter 4, respectively. From equation (1), we have:

{overscore (S)} ₁(2 i+1)=−P{overscore (s)} ₂*(2 i), {overscore (s)} ₂(2 i+1)=P{overscore (s)} ₁*(2 i),   (2)

[0026] which shows that each transmitted block from one antenna at time slot 2 i+1 is a conjugated and permuted version of the corresponding transmitted block from the other antenna at time slot 2 i (with a possible sign change). For flat fading channels, symbol blocking is unnecessary, i.e., J=K=1 and P=1, and the design of (1) reduces to the Alamouti ST code matrix. However, for frequency selective multipath channels, the permutation matrix P is necessary as will be clarified soon.

[0027] To avoid inter-block interference in the presence of frequency selective multipath channels, transmitter 4 insert a cyclic prefix for each block before transmission. Mathematically, at each antenna μ∈ [1,2], a tall P×J transmit-matrix T_(cp):=[I_(cp) ^(T), I_(J) ^(T)]^(T), with I_(cp) comprising the last P−J rows of I_(J), is applied on {overscore (s)}_(μ)(i) to obtain P×1 blocks: u_(μ)(i)=T_(cp){overscore (s)}_(μ)(i). Indeed, multiplying T_(cp) with {overscore (s)}_(μ)(i) replicates the last P−L entries-of {overscore (s)}_(μ)(i) and places them on its top. The transmitted sequences from both antennas of transmitter 4 are depicted in FIG. 2.

[0028] With symbol rate sampling, h_(μ):=[h_(μ)(0); . . . ; h_(μ)(L)]^(T) be the equivalent discrete-time channel impulse response (that includes transmit-receive filters as well as multipath effects) between the μth transmit antenna and the single receive antenna, where L is the channel order. With the CP length at least as long as the channel order, P−J=L, the inter block interference (IBI) can be avoided at the receiver by discarding the received samples corresponding to the cyclic prefix. CP insertion at the transmitter together with CP removal at the receiver yields the following channel input-output relationship in matrix-vector form: x(i) $\begin{matrix} {{{x(i)} = {{\sum\limits_{\mu = 1}^{2}{{\overset{\sim}{H}}_{\mu}{{\overset{\_}{s}}_{\mu}(i)}}} + {w(i)}}},} & (3) \end{matrix}$

[0029] where the channel matrix {tilde over (H)}_(μ) is circulant with [{tilde over (H)}_(μ)]_(p,q)=h_(μ)((p−q) mod J), and the additive Gaussian noise w(i) is assumed to be white with each entry having variance σ_(ω)² = N₀.

[0030] Receiver 6 can exploit the following two properties of circulant matrices:

[0031] p1) Circulant matrices can be diagonalized by FFT operations

{tilde over (H)} _(μ) =F _(J) ^(H) D({tilde over (h)} _(μ))F _(J) and {tilde over (H)} _(μ) ^(H) =F _(J) ^(H) D({tilde over (h)} _(μ)*)F _(J)  , (4)

[0032] where D({tilde over (h)}_(μ)):=diag({tilde over (h)}_(μ)), and {tilde over (h)}_(μ):=[H_(μ)(e^(j0)), H_(μ)(e^(j 2π/J)), . . . , H_(μ)(e^(j 2π/J(J−1)))]^(T) with the pth entry being the channel frequency response H_(μ)(z):=Σ_(t=0) ^(L)h_(μ)(l)z^(−l) evaluated at the frequency z=e^(j 2π/J(p−1)).

[0033] p2) Pre- and post-multiplying {tilde over (H)}_(μ) by P yields {tilde over (H)}_(μ) ^(T):

P{tilde over (H)}_(μ)P={tilde over (H)}_(μ) ^(T) and P{tilde over (H)}_(μ)*P={tilde over (H)}_(μ) ^(H)  . (5)

[0034] With the ST coded blocks satisfying (2), let us consider two consecutive received blocks [c.f. (3)]:

x(2 i)=H ₁ {overscore (s)} ₁(2 i)+H ₂ {overscore (s)} ₂(2 i)+w(2 i),   (6).

x(2 i+1)=−{tilde over (H)} ₁ P{overscore (s)} ₂*(2 i)+{tilde over (H)} ₂ P{overscore (s)} ₁*(2 i)+w(2 i+1).   (7)

[0035] Left-multiplying (7) by P, conjugating, and using p2), we arrive at:

Px*(2 i+1)=−{tilde over (H)} ₁ ^(H) {overscore (s)} ₂(2 i)+{tilde over (H)} ₂ ^(H) {overscore (s)} ₁(2 i)+Pw*(2 i+1).   (8)

[0036] Notice that having permutation matrix P inserted at the transmitter allows the Hermitian of the channel matrices in (8) for enabling multi-antenna diversity gains with linear receiver processing.

[0037] We will pursue frequency-domain processing of the received blocks, which we describe by multiplying the blocks x(i) with the FFT matrix F_(J) that implements the J-point FFT of the entries in x(i). Let us define y(2 i):=F_(J)x(2 i), y2(2 i+1):=F_(J)Px*(2 i+1), and likewise {overscore (η)}(2 i):=F_(J)w(2 i) and {overscore (η)}*(2 i+1):=F_(J)Pw2(2 i+1). For notational convenience wire also define the diagonal matrices D₁:=D({tilde over (h)}₁) and D₂:=D({tilde over (h)}₂) with the corresponding transfer function FFT samples on their diagonals. Applying the property p1)on (6) and (8), we obtain the FFT processed blocks as:

y(2 i)=D ₁ F _(J) {overscore (s)} ₁(2 i)+D ₂ F _(J) {overscore (s)} ₂(2 i)+{overscore (η)}(2 i),   (9)

y 2(2 i+1)=−D ₁ *F _(J) {overscore (s)} ₂(2 i)+D ₂ *F _(J) {overscore (s)} ₁(2 i)+{overscore (η)}*(2 i+1).   (10)

[0038] It is important to remark at this point that permutation conjugation, and FFT operations on the received blocks x(i) do not introduce any information loss, or color the additive noises in (9) and (10) that remain white It is thus sufficient to rely only on the FFT processed blocks y(2 i) and y*(2 i+1) when performing symbol detection.

[0039] After defining {grave over (y)}(i):=[y^(T)(2 i), y^(H)(2 i+1)]^(T), we can combine (9) and (10) into a single block matrix-vector form to obtain: $\begin{matrix} {{{\overset{.}{y}(i)} = {{\underset{\underset{:=}{}}{\begin{bmatrix} _{1} & _{2} \\ _{2}^{*} & {- _{1}^{*}} \end{bmatrix}}\quad\begin{bmatrix} {F_{J}{s\left( {2i} \right)}} \\ {F_{J}{s\left( {{2i} + 1} \right)}} \end{bmatrix}} - \begin{bmatrix} {\overset{\_}{\eta}\left( {2i} \right)} \\ {{\overset{\_}{\eta}}^{*}\left( {{2i} + 1} \right)} \end{bmatrix}}},} & (11) \end{matrix}$

[0040] where the identities {overscore (s)}₁(2 i)=s(2 i) and {overscore (s)}₂(2 i)=s(2 i+1) have been used following our design in (1).

[0041] Consider a J×J diagonal matrix {overscore (D)}₁₂ with non-negative diagonal entries as: {overscore (D)}₁₂=[D₁*D₁+D₂*D₂]^(½). We can verify that the matrix D in (11) satisfies D^(H)D=I₂{circle over (×)}{overscore (D)}₁₂ ², where {circle over (×)} stands for Kronecker product. Based on D₁ and D₂, we next construct a unitary matrix U. If h₁ and h₂ do not share common zeros on the FFT grid {o^(j 2π/J n)}_(n=0) ^(J−1), then {overscore (D)}₁₂ is invertible, and we select U as U:=D(I₂{circle over (×)}{overscore (D)}₁₂ ⁻¹). If h₁ and h₂ happen to share common zero(s) on the FFT grid (although this event has probability zero), then we construct U as follows. Supposing without of loss of generality that h₁ and h₂ share a common zero at the first subcarrier e^(j0), we have that [D₁]_(1,1)=[D₂]_(1,1)=[{overscore (D)}₁₂]_(1,1)=0. We then construct a diagonal matrix D₁′ which differs from D₁ only at the first diagonal entry: [D₁′]_(1,1)=1. Similar to the definition of D and {overscore (D)}₁₂, we construct D′ and {overscore (D)}₁₂′ by substituting D₁ with D₁′. Because {overscore (D)}₁₂′ is invertible, we form U:=D′[I₂{circle over (×)}({overscore (D)}₁₂′)⁻¹]. In summary, no matter whether {overscore (D)}₁₂ is invertible or not, we can always construct a unitary U, which satisfies U^(H)U=I_(2J) and U^(H)D=I₂{circle over (×)}{overscore (D)}₁₂, where the latter can be easily verified. As multiplying by unit matrices does not incur any loss of decoding optimality in the presence of additive white Gaussian noise, (11) yields {haeck over (z)}(i):=[z^(T)(2 i).z^(T)(2 i+1)]^(T) as: $\begin{matrix} {{{\overset{.}{z}(i)} = {{U^{\mathcal{H}}{\overset{.}{y}(i)}} = {\begin{bmatrix} {{\overset{\_}{}}_{12}F_{J}{s\left( {2i} \right)}} \\ {{\overset{\_}{}}_{12}F_{J}{s\left( {{2i} + 1} \right)}} \end{bmatrix} + {U^{\mathcal{H}}\begin{bmatrix} {\overset{\_}{\eta}\left( {2i} \right)} \\ {{\overset{\_}{\eta}}^{*}\left( {{2i} + 1} \right)} \end{bmatrix}}}}},} & (12) \end{matrix}$

[0042] where the resulting noise {grave over (η)}(i):=[η^(T)(2 i), η^(T)(2 i+1)]^(T)=U^(H)[{overscore (η)}^(T)(2 i), {overscore (η)}^(H)(2 i+1)]^(T) is still white with each entry having variance. N₀.

[0043] We infer from (12) that the blocks s(2 i) and s(2 i+1) can be demodulated separately without compromising the ML optimality, after linear receiver processing. Indeed, so far we applied at the receiver three linear unitary operations after the CP removal: i) permutation (via P); ii) conjugation and FFT (via F_(J)); and iii) unitary combining (via U^(H)). As a result, we only need to demodulate each information block d(i) separately from the following sub-blocks [c.f (12)]:

z(i)={overscore (D)} ₁₂ F _(J) s(i)+η(i)={overscore (D)} ₁₂ F _(J) Θd(i)+η(i).   (13)

[0044] A. Diversity Gain Analysis

[0045] Let us drop the block index i from (13), and e.g., use d to denote d(i) for notational brevity. With perfect CSI at the receiver, we will consider the pairwise error probability (PEP) P(d→d′|h₁, h₂) that the symbol block d is transmitted, but is erroneously decoded as d′≠d. The PEP can be approximated using the Chernoff bound as

P(s→s′|h ₁ , h ₂)≦exp(−d ²(z,z′)/4N ₀),   (14)

[0046] where d(z, z′) denotes the Euclidean distance between z and z′.

[0047] Define the error vector as e:=d−d′, and a J×(L+1) Vandermonde matrix V with [V]_(p,q)=exp(−j2πpq/J). The matrix V links the channel frequency response with the time-domain channel taps as {tilde over (h)}_(μ)=Vh_(μ). Staring with (13), we then express the distance as: $\begin{matrix} {\begin{matrix} {{d^{2}\left( {z,z^{\prime}} \right)} = {{{{\overset{\_}{}}_{12}F_{J}\Theta \quad e}}^{2} = {e^{\mathcal{H}}\Theta^{\mathcal{H}}F_{J}^{\mathcal{H}}{\overset{\_}{}}_{12}^{2}F_{J}\Theta \quad e}}} \\ {{= {{\sum\limits_{\mu = 1}^{2}{{_{\mu}F_{J}\Theta \quad e}}^{2}} = {\sum\limits_{\mu = 1}^{2}{{_{e}V\quad h_{\mu}}}^{2}}}},} \end{matrix}\quad} & (15) \end{matrix}$

[0048] where D_(e):=diag(F_(J)Θe) such that D_(μ)F_(J)Θe=D_(e){tilde over (h)}_(μ)=D_(e)Vh_(μ).

[0049] We focus on block quasi static channels, i.e., channels that remain invariant over each space-time code bock, but may vary from one block to the next. We further adopt the following assumption:

[0050] as0)) the channels h₁ and h₂ are uncorrelated; and for each antenna μ ∈ [1,2], the channel h_(μ) is zero-mean, complex Gaussian distributed, with covariance matrix R_(h, μ):=E{h_(μ)h_(μ) ^(H)}.

[0051] If the entries of h_(μ) are i.i.d., then we have R_(h,μ)=I_(L−1)/(L+1), where the channel covariance matrix is normalized to have unit energy; i.e., tr{R_(h,μ)}=1. Because general frequency selective multipath channels have covariance matrices with arbitrary rank, we define the “effective channel order” as: {tilde over (L)}_(μ)=rank(R_(h,μ))−1. Consider now the following eigen decomposition:

R _(h,μ) =U _(h,μ)Λ_(h,μ) U _(h,μ) ^(H),   (16)

[0052] where Λ_(h,μ) is an ({tilde over (L)}_(μ)+1)×({tilde over (L)}_(μ)+1) diagonal matrix with the positive eigenvalues of R_(h,μ) on its diagonal, and U_(h,μ)is an (L+1)×({tilde over (L)}+1) matrix having orthonormal columns: U_(h,μ) ^(H)U_(h,μ)=I_({tilde over (L)}μ+1). Defining {overscore (h)}_(μ)=Λ_(h,μ) ^(−½)U_(h,μ) ^(H)h_(μ),we can verify that the entries of {overscore (h)}_(μ)are i.i.d, with unit variance. Since h_(μ) and U_(h,μ)Λ_(h,μ) ^(½){overscore (h)}_(μ) have identical distributions, we replace the former by the latter in the ensuing PEP analysis. A special case of interest corresponds to transmissions experiencing channels with full rank correlation matrices; i.e., rank(R_(h,μ))=L+1 and {tilde over (L)}_(μ)=L. As will be clear later on, a rich scattering environment leads to R_(h,μ)'s with full rank, which is favorable in broadband wireless applications because it is also rich in diversity.

[0053] With the aid of the whitened and normalized channel vector {overscore (h)}_(μ), we can simplify (15) to:

d ²(z,z′)=∥D _(e) VU _(h,1)Λ_(h,1) ^(½){overscore (h)}₁∥² +∥D _(e) VU _(h,2)Λ_(h,2) ^(½) {overscore (h)} ₂.   (17)

[0054] From the spectral decomposition of the matrix A_(e,μ) ^(H)A_(e,μ), where A_(e,μ):=D_(e)VU_(h,μ)Λ_(h,μ) ^(½), we know that there exists a unitary matrix U_(e,μ) such that U_(e,μ) ^(H)A_(e,μ) ^(H)A_(e,μ)U_(e,μ)=Λ_(e,μ), where Λ_(e,μ) is diagonal with non-increasing diagonal entries collected in the vector λ_(e,μ):=[λ_(e,μ()0), λ_(e,μ)(1), . . . , λ_(e,μ)({tilde over (L)}_(μ))]^(T).

[0055] Consider now the channel vectors {overscore (h)}_(μ)′:=U_(e,μ) ^(H){overscore (h)}_(μ), with identity correlation matrix. The vector {overscore (h)}_(μ)′ is clearly zero-mean, complex Gaussian, with i.i.d entries. Using {overscore (h)}_(μ)′, we can rewrite (17) as: $\begin{matrix} {{d^{2}\left( {z,z^{\prime}} \right)} = {{\sum\limits_{\mu = 1}^{2}{\left( {\overset{\_}{h}}_{\mu}^{\prime} \right)^{\mathcal{H}}U_{e_{n}u}^{\mathcal{H}}A_{e_{n}u}^{\mathcal{H}}A_{e_{n}u}U_{e_{n}u}{\overset{\_}{h}}_{\mu}^{\prime}}} = \left. {\sum\limits_{l = 1}^{{\overset{\_}{L}}_{1}}{\lambda_{e,1}(l)}} \middle| {{\overset{\_}{h}}_{1}^{\prime}(l)} \middle| {}_{2}{+ {\sum\limits_{l = 1}^{{\overset{\_}{L}}_{2}}{\lambda_{e,2}(l)}}} \middle| {{\overset{\_}{h}}_{2}^{\prime}(l)} \middle| {}_{2}. \right.}} & (18) \end{matrix}$

[0056] Based on (18), and by averaging (14) with respect to the i.i.d. Rayleigh random variables |{overscore (h)}₁′(l)|, |{overscore (h)}₂′(l)|, we can upper bound the average PEP as follows: $\begin{matrix} {{P\left( s\rightarrow s^{\prime} \right)} \leq {\prod\limits_{l = 0}^{{\overset{\_}{L}}_{1}}{\frac{1}{1 + {{\lambda_{e,1}(l)}/\left( {4N_{0}} \right)}}{\prod\limits_{l = 0}^{{\overset{\_}{L}}_{2}}{\frac{1}{1 + {{\lambda_{e,2}(l)}/\left( {4N_{0}} \right)}}.}}}}} & (19) \end{matrix}$

[0057] If r_(e,μ) is the rank of A_(e,μ) (and thus the rank of A_(e,μ) ^(H)A_(e,μ)), then λ_(e,μ)(l)≠0 if and only if l ∈[0, τ_(e,μ)−1]. It thus follows from (19) that $\begin{matrix} {{P\left( s\rightarrow s^{\prime} \right)} \leq {\left( \frac{1}{4N_{0}} \right)^{- {({r_{e,1} + r_{e,2}})}}{\left( {\prod\limits_{l = 0}^{r_{e,1} - 1}{{\lambda_{e,1}(l)}{\prod\limits_{l = 0}^{r_{e,2} - 1}{\lambda_{e,2}(l)}}}} \right)^{- 1}.}}} & (20) \end{matrix}$

[0058] We call r_(e):=r_(e,1)+r_(e,2) the diversity gain G_(d,e), and [529 _(t=0) ^(r) ^(_(e,1)) ⁻¹λ_(e,1)(l)Π_(l=0) ^(r) ^(_(a,2)) ⁻¹λ_(e,2)(l)]^({fraction (1/r)}) ^(_(e)) the coding gain G_(c,e) of the system for a given symbol error vector e. The diversity gain G_(d,e) determines the slope of the averaged (w.r.t. the random channel) PEP (between s and s′) as a function of the signal to noise ratio (SNR) at high SNR (N₀→0). Correspondingly, G_(c,e) determines the shift of this PEP curve in SNR relative to a benchmark error rate curve of [1/(4N₀)]^(−r) ^(_(e)) . Without relying on PEP to design (nonlinear) ST codes for flat fading channels, we here invoke PEP bounds to prove diversity properties of our proposed single-carrier block transmissions over frequency selective channels.

[0059] Since both G_(d,e) and G_(c,e) depend on the choice of e (thus on s and s′), we define the diversity and coding gains for our system, respectively, as: $\begin{matrix} {G_{d}:={{\min\limits_{e \neq 0}{G_{d,c,}\quad a\quad n\quad d\quad G_{c}}}:={\min\limits_{e \neq 0}{G_{c,c}.}}}} & (21) \end{matrix}$

[0060] Based on (21), one can check both diversity and coding gains. However, in this paper we focus only on the diversity gain. First we observe that the matrix A_(e,μ) ^(H)A_(e,μ) is square of size ({tilde over (L)}_(μ)+1). Therefore, the maximum achievable diversity gain in a two transmit- and one receive-antennae system is G_(d)=Σ_(μ=L) ²({tilde over (L)}_(μ)+1) for FIR channels with effective channel order {tilde over (L)}_(μ), μ=1,2, while it becomes 2(L+1) in rich scattering environments. This maximum diversity can he easily achieved by e.g., a simple redundant transmission where each antenna transmits the same symbol followed by L zeros in two non-overlapping time slots. We next examine the achieved diversity levels in our following proposed schemes, which certainly have much higher rate than redundant transmissions.

[0061] B. CP-Only

[0062] We term CP-only the block transmissions with no precoding; Θ=I_(K), J=K, and s(i)=d(i). The word “only” emphasizes that, unlike OFDM, no IFFT is applied at the transmitter. Let us now check the diversity order achieved by CP-only. The worst case is to select d=a1_(J×1) and d′=a′1_(J×1) implying e=(a−a′)1_(J×1), where a,a′ ∈ A. Verifying that for these error events, the matrix D_(e)=diag(F_(J)e) has only one non-zero entry, we deduce that r_(e,1)=r_(e,2)=1. Therefore, the system diversity order achieve by CP-only is G_(d)=2. This is nothing but space-diversity of order two coming from the two transmit antennas [c.f. (13)]. Note that CP-only schemes suffer from loss of multipath diversity.

[0063] To benefit also fiom the embedded multipath-induced diversity, we have to modify our transmissions.

[0064] C. Linearly Precoded CP-only

[0065] To increase our ST system's diversity order, transmitter 4 may utilize linear precoding developed originally for single-antenna transmissions. One can view CP-only as a special case of the linearly precoded CP-only system (denoted henceforth as LP-CP-only) with identity precoder. With s(i)=Θd(i) and carefully designed Θ≠I_(K), we next show that the maximum diversity is achieved. We will discuss two cases: the first one introduces no redundancy because it uses J=K, while the second one is redundant and adopts J=K+L. For non-redundant precoding with J=K, it has been established that for any signal constellation adhering to a finite alphabet, there always exists a K×K unitary constellation rotating (CR) matrix ensuring Θ_(CR) that each entry of Θ_(CR)(d−d′) is non-zero for any pair of (d,d ′). We thus propose to construct Θ=F_(K) ^(H)Θ_(cr) such that F_(K)Θ=Θ_(cr). With this construction D_(c)=diag(Θ_(cr)e) is guaranteed to have non-zero entries on its diagonal, and thus it has fill rank. Consequently, the matrix D_(c)V has full column rank L+1, and A_(e,μ)=D_(e)VU_(h,μ)Λ_(h,μ) ^(½) has full column rank r_(e,μ)={overscore (L)}_(μ+)1. Hence, the maximum achievable diversity order is indeed achieved.

[0066] We emphasize here that the non-redundant precoder Θ_(cr) is constellation dependent. For commonly use BPSK, QPSK and all QAMs constellations, an for the block size K equal to a power of 2:K=2², one class of Θ_(cr) precoders with large coding gains is found to be:

Θ_(cr) =F _(K)Δ(α) and thus, Θ=Δ(α),   (22)

[0067] where Δ(α):=diag(1, α, . . . , α^(K−1)) with α ∈ {e^(fπ/2h(1+4n))}_(n=0) ^(K−1). For block size K≠2², one can construct Θ_(cr) by truncating a larger unitary matrix constructed as in (22). The price paid for our increased diversity gain is that LP-CP-only does not offer constant modulus transmissions, in general. However, by designing K to be a power of 2, and by choosing Θ as in (22), the transmitted signals s(i)=Δ(α)d(i) are constant modulus if d(i) are PSK signals. Therefore, by selecting K to be a power of 2, we can increase the diversity gain without reducing power efficiency.

[0068] Alternatively, we can adopt a redundant J×K precoder Θ with J=K+L. Our criterion for selecting such tall precoding matrices Θ is to guarantee that F_(J)Θ satisfies the following property: any K rows of F_(J)Θ are linearly independent One class of F_(J)Θ satisfying this property includes Vandermonde matrices Θ_(van) with distinct generators [p₁, . . . , p_(J)], defined as: $\begin{matrix} {{\Theta_{van} = {\frac{1}{\sqrt{J}}\begin{bmatrix} 1 & \rho_{1}^{- 1} & \cdots & \rho_{1}^{- {({K - 1})}} \\ \vdots & \vdots & ⋰ & \vdots \\ 1 & \rho_{J}^{- 1} & \cdots & \rho_{J}^{- {({K - 1})}} \end{bmatrix}}},{a\quad n\quad d\quad t\quad h\quad u\quad s},{\Theta = {F_{J}^{\mathcal{H}}{\Theta_{van}.}}}} & (23) \end{matrix}$

[0069] With F_(J)Θ=Θ_(van), we have that Θ_(van)e has at least (L+1) nonzero entries for any e regardless of the underlying signal constellation. Indeed, if Θ_(van)e has only L nonzero entries for some e, then it has K zero entries. Picking the corresponding K rows of Θ_(van) to form the truncated matrix {overscore (Θ)}_(van), we have {overscore (Θ)}_(van)e=0, which shows that these K rows are linearly dependent, thus violating the design of the precoder Θ_(van). With D_(e)=diag(Θ_(van)e) having at least (L+1) nonzero entries, the matrix D_(c)V has full rank because any L+1 rows of V are linearly independent. Thus the maximum diversity gain is achieved with redundant precoding irrespective of tie underlying constellation.

[0070] When J ∈[K,K+L], constellation irrespective precoders are impossible because Θe can not have L+1 nonzero entries for any e that is unconstrained. Therefore, constellation independent precoders are not possible for J<K+L. However, with some redundancy J>K, the design of constellation-dependent precoders may become easier.

[0071] D. Affine Precoded CP-only

[0072] Another interesting class of linear precoders implements an affine transformation: s(i)=Θd(i)+Θ′b(i), where b(i) is a known symbol vector. In this paper, we are only interested in the special form of: $\begin{matrix} {{{s(i)} = {{{T_{1}{d(i)}} + {T_{2}{b(i)}}} = \begin{bmatrix} {d(i)} \\ {b(i)} \end{bmatrix}}},} & (24) \end{matrix}$

[0073] where the precoder Θ=T₁ is the first K columns of I_(J), the precoder Θ′=T₂ is the last L columns of I_(J), and the known symbol vector b has size L×1 with entries drawn from the same alphabet A. We henceforth term the transmission format in (24) as AP-CP-only. Notice that in this scheme, J=K+L and P=J+L.

[0074] Although here we place b(i) at the bottom of s(i) for convenience, we could also place b(i) at arbitrary positions within s(i). As long as L consecutive symbols are known in s(i), all decoding schemes detailed in Section II are applicable.

[0075] Recall that the error matrix D_(a)=diag(F_(J)T₁e) does not contain known symbols. Since F_(J)T₁ is a Vandermonde matrix of the form (23), the maximum diversity gain is achieved, as discussed in Section I-C for redundant LP-CP-only.

[0076] In the CP-based schemes depicted in Fi, 2, the CP portion of the transmitted sequence is generally unknown, because it is replicated from the unknown data blocks. However, with AP-CP-only in (24), and with the specific choice of P=P_(J) ^((K)), we have P_(J) ^((K))s(i)=[[P_(K) ⁽⁰⁾d(i)]^(T), [P_(L) ⁽⁰⁾b(i)]^(T)]^(T), which implies that both the data block and the known symbol block are time reversed, but keep their original positions. The last L entries of P_(J) ^((K))s(i) are again known, and are then replicated as cyclic prefixes. For this special case, we depict the transmitted sequences in FIG. 3. In this format, the data block d(i) is surrounded by two known blocks, that correspond to the pre-amble and post-amble. Our general design based on the CP structure includes this known pre an post-ambles as a special case. Notice that the pre-amble and post-amble have not been properly designed in some conventional systems. The consequence is that “edge effects” appear for transmissions with finite block length, and an approximation on the order of O (L/J) has to be made in order to apply Viterbi's decoding algorithm. This approximation amounts to nothing but the fact that a linear convolution can be approximated by a circular convolution when the block size is much larger than the channel order. By simply enforcing a CP structure to obtain circulant convolutions, Viterbi's algorithm can be applied to our proposed AP-CP-only with no approximation whatsoever, regardless of the block length and the channel order, as will be clear soon.

[0077] E. ZP-Only

[0078] Suppose now that in AP-CP-only, we let b(i)=0 instead of having known symbols drawn from the constellation alphabet, and we fix P=P_(J) ^((K)). Now, the adjacent data blocks are guarded by two zero blocks, each having length L, as depicted in FIG. 3 Since the channel has only order L, presence of 2L zeros in the middle of two adjacent data blocks is not necessary. Keeping only a single block of L zeros corresponds to removing the CP-insertion operation at the transmitter. On the other hand, one could view that the zero block in the previous block serves the CP for the current block, and thus all derivations done for CP-based transmission are still valid. The resulting transmission format is shown in FIG. 4, which achieves higher bandwidth efficiency than AP-CP-only. We term this scheme as ZP-only, where J=K+L and P=J.

[0079] By mathematically viewing ZP-only as a special case of AP-CP-only with b(i)=0, it is clear that the maximum diversity is achieved. In addition to the rate improvement, ZP-only also saves the transmitted power occupied by CP and known symbols,

[0080] For convenience, we list all aforementioned schemes in Table I, assuming a rich scattering environment. Power loss induced by the cyclic prefix and the know symbols, is also considered. It certainly becomes negligible when K>>L.

[0081] F. Links with Multicarrier Transmissions

[0082] In this section, we link single carrier with digital multicarrier (OFDM based, schemes. We first examine the transmitted blocks on two consecutive time intervals, For LP-CP-only, the transmitted space-time matrix is: $\begin{matrix} {{\begin{bmatrix} {u_{1}\left( {2i} \right)} & {u_{1}\left( {{2i} + 1} \right)} \\ {u_{2}\left( {2i} \right)} & {u_{2}\left( {{2i} + 1} \right)} \end{bmatrix} = {\begin{bmatrix} {T_{c\quad p}\Theta \quad {d\left( {2i} \right)}} & {{- T_{c\quad p}}P\quad \Theta^{*}\quad {d^{*}\left( {{2i} + 1} \right)}} \\ {T_{c\quad p}\Theta \quad {d\left( {{2i} + 1} \right)}} & {T_{c\quad p}P\quad \Theta^{*}\quad {d^{*}\left( {2i} \right)}} \end{bmatrix}\quad \begin{matrix} \left. \rightarrow\quad {t\quad i\quad m\quad e} \right. \\ {\left. \downarrow\quad s \right.\quad p\quad a\quad c\quad e} \end{matrix}}}\quad,} & (25) \end{matrix}$

[0083] If let P=P_(J) ⁽¹⁾ and Θ=F_(J) ^(H)Ψ, we obtain for a general matrix Ψ: $\begin{matrix} {\begin{bmatrix} {u_{1}\left( {2i} \right)} & {u_{1}\left( {{2i} + 1} \right)} \\ {u_{2}\left( {2i} \right)} & {u_{2}\left( {{2i} + 1} \right)} \end{bmatrix} = {\begin{bmatrix} {T_{c\quad p}F_{J}^{\mathcal{H}}\Psi \quad {d\left( {2i} \right)}} & {{- T_{c\quad p}}F_{J}^{\mathcal{H}}\Psi^{*}\quad {d^{*}\left( {{2i} + 1} \right)}} \\ {T_{c\quad p}F_{J}^{\mathcal{H}}\Psi \quad {d\left( {{2i} + 1} \right)}} & {T_{c\quad p}F_{J}^{\mathcal{H}}\Psi^{*}\quad {d^{*}\left( {2i} \right)}} \end{bmatrix}\quad {\begin{matrix} \left. \rightarrow\quad {t\quad i\quad m\quad e} \right. \\ {\left. \downarrow\quad s \right.\quad p\quad a\quad c\quad e} \end{matrix}\quad.}}} & (26) \end{matrix}$

TABLE 1 SUMMARY OF SINGLE CARRIER SCHEMES IN RICH-SCATTERING ENVIRONMENTS Rate R Diversity G_(d) Power Loss (dB) Features CP-only $\frac{K}{K + L}\log_{2}{}$

2 $10\quad \log_{10}\frac{K + L}{K}$

constant modulus (C-M)* non-redundant LP-CP-only $\frac{K}{K + L}\log_{2}{}$

2(L + 1) $10\quad \log_{10}\frac{K + L}{K}$

constellation-specific precoder constant modulus redundant LP-CP-only $\frac{K}{K + {2L}}\log_{2}{}$

2(L + 1) $10\quad \log_{10}\frac{K + L}{K}$

constellation-independent Not C-M in general AP-CP-only $\frac{K}{K + {2L}}\log_{2}{}$

2(L + 1) $10\quad \log_{10}\frac{K + {2L}}{K}$

constellation-independent constant modulus ZP-only $\frac{K}{K + L}\log_{2}{}$

2(L + 1) 0 constellation-independent C-M except zero guards

[0084] If Ψ=I_(K), then (26) corresponds to the space-time block coded OFDM proposed in Y. Li, J. C. Chung,and N. R. Sollenberger, “Transmitter diversity for OFDM systems and its impact on high-rate data wireless networks,” IEEE Journal on Selected Areas in Communications, vol. 17, no.7, pp. 1233-1243, July 1999. Designing Ψ≠I_(K) introduces linear precoding across OFDM subcarriers, as proposed in other conventional techniques. Therefore, LP-CP-only includes linear precoded space-time OFDM as a special case by selecting the precoder Φand the permutation P appropriately. Although linear precoding has been proposed for space time OFDM systems, the diversity analysis has not been provided, The link we introduce here reveals that the maximum diversity gain is also achieved by linearly precoded ST-OFDM with the Vandermonde precoders.

[0085] Interestingly, linearly precoded OFDM can even be converted to zero padded transmissions. Indeed, choosing Ψ to be the first K columns of F_(J), we obtain the transmitted block as: u(i)=T_(cp)F_(J) ^(H)Ψd(i)=[0_(L×1) ^(T), d^(T)(i), 0_(L×1) ^(T)]^(T), which inserts zeros both at the top and at the bottom of each data block.

[0086] G. Capacity Result

[0087] We now analyze the capacity of the space time block coding format of (1). The equivalent channel input-output relationship, after receiver processing, is described by (13) as: z={overscore (D)}₁₂F_(J)s+η, where we drop the block index for brevity. Let I(z;s) denote the mutual information between z and s, and recall that I(z;s) is maximized when s is Gaussian distribute. Due to tie lack of channel knowledge at the transmitter, the transmission power is equally distributed among symbols, with R₂=E{ss^(H)}=σ₂ ²I_(J). Taking into account the CP of length L. the channel capacity, for a fixed channel realization, is thus: $\begin{matrix} {\begin{matrix} {C_{J} = {{\frac{1}{J + L}\max \quad {\mathcal{I}\left( {z;s} \right)}}\quad = {\frac{1}{J + L}\log_{2}d\quad e\quad {t\left( {I_{J} + {\frac{\sigma_{e}^{2}}{N_{0}}{\overset{\_}{}}_{12}F_{J}F_{J}^{\mathcal{H}}{\overset{\_}{}}_{12}}} \right)}}}} \\ {\quad {= {\frac{1}{J + L}{\sum\limits_{n = 0}^{J - 1}{{\log_{2}\left( {1 + {\frac{\sigma_{e}^{2}}{N_{0}}\left( \left| {H_{1}\left( ^{j2\frac{\Sigma\Delta}{j}} \right)} \middle| {}_{2}{+ \left| {H_{2}\left( ^{j2\frac{\Sigma\Delta}{j}} \right)} \right|^{2}} \right. \right)}} \right)}.}}}}} \end{matrix}\quad} & (27) \end{matrix}$

[0088] Define E₂=2σ₂ ² as the total transmitted power from two antennas per channel use. As the block size J increases, we obtain $\begin{matrix} {C_{J - {\gamma\infty}} = {\int_{0}^{1}{{\log_{2}\left( {1 + {\frac{E_{s}}{2N_{0}}\left( \left| {H_{1}\left( ^{{j2\pi}\int} \right)} \middle| {}_{2}{+ \left| {H_{2}\left( ^{{j2\pi}\int} \right)} \right|^{2}} \right. \right)}} \right)}{{f}.}}}} & (28) \end{matrix}$

[0089] The capacity for frequency selective channels with multiple transmit and receive antennas has been described with conventional techniques. The result in (28) coincides with that of some of these techniques when we have two transmit antennas and one receive antenna. Therefore, our proposed transmission format in (1) does not incur capacity loss in this special case. This is consistent with techniques where the Alamouti coding is shown to achieve capacity for frequency-flat fading channels with such an antenna configuration. To achieve capacity for systems with two transmit antennas and a single receive antenna, it thus suffices to deploy suitable one-dimensional channel codes, or scalar codes.

[0090] II. Equalization and Decoding

[0091] Let {overscore (z)}(i):=z(i) for CP-only, LP-CP-only, ZP-only, and {overscore (z)}(i):=z(i)−{overscore (D)}₁₂F_(J)T₂b(i) for AP-CP-only. With this convention, we can unify the equivalent system output after the linear receiver processing as:

{overscore (z)}(i)=F _(J) Θd(i)+η(i)=Ad(i)+η(i),   (29)

[0092] where A:=F_(J)Θ, the noise η(i) is whim with covariance σ_(w) ²I_(J), and the corresponding Θ is defined as in Section I.

[0093] Brute-force ML decoding applied to (29) requires |A|^(K) enumerations, which becomes certainly prohibitive as the constellation size |A| and/or the block length K increases. A relatively faster near-ML search is possible with the sphere decoding (SD) algorithm, which only searches for vectors that are within a sphere centered at the received symbols. The theoretical complexity of SD is polynomial in K, which is lower than exponential, but still too high for K>16. Rely when the block size K is small, the SD equalizer can be adopted to achieve near-ML performance at a manageable complexity. The unique feature of SD is that the complexity does not depend on the constellation size. Thus, SD is suitable for systems with small block size K, but with large signal constellations.

[0094] We now turn our attention to low-complexity equalizers by trading off performance with complexity. Linear zero forcing (ZF) and minimum mean square error (MMSE) block equalizers certainly offer low complexity alternatives. The bock MMSE equalizer is:

Γ_(mmse)=(A ^(H) A+σ _(w) ²/σ₂ ²I_(K))⁻¹ A ^(H),   (30)

[0095] where we have assumed that the symbol vectors are while with covariance matrix R₂=Ε{s(i)s^(H)(i)}=σ₂ ²I_(K). The MMSE equalizer reduces to the ZF equalizer by setting σ_(w) ²=0 in (30).

[0096] For non-redundant LP-CP-only with Θ=Δ(α), we further simplify (30) to

Γ_(mmse)=Δ(α*)F _(K) ^(H)[{overscore (D)}₁₂ ²+σ_(w) ²/σ₂ ²I_(K)]⁻¹{overscore (D)}₁₂,   (31)

[0097] A. ML Decoding for AP-CP-Only and ZP-Only

[0098] For AP-CP-only and ZP-only, we have

z={overscore (D)} ₁₂ F _(J) s+η,   (32)

[0099] where we drop the block index i for simplicity. Distinct from other systems, AP-CP-only and ZP-only assure that s has the last L entries known, and the first K entries drawn from the finite alphabet, A.

[0100] In the presence of white noise, ML decoding can be expressed as:

{tilde over (s)} _(ML)=arg max ln P(z|s)=arg max {−∥z−{tilde over (D)} ₁₂ F _(J) s∥ ² /N ₀}.   (33)

[0101] We next simplify (33), starting with $\begin{matrix} {\begin{matrix} {{- {{z - {{\overset{\_}{}}_{12}F_{J}s}}}^{2}} = {{2R\quad e\left\{ {s^{\mathcal{H}}F_{J}^{\mathcal{H}}{\overset{\_}{}}_{12}z} \right\}} - {s\quad F_{J}^{\mathcal{H}}{\overset{\_}{}}_{12}^{2}F_{J}s} - {z^{\mathcal{H}}z}}} \\ {{= {{2R\quad e\left\{ {s^{\mathcal{H}}r} \right\}} - {\sum\limits_{\mu = 1}^{2}{{{\overset{\_}{H}}_{\mu}s}}^{2}} - {z^{\mathcal{H}}z}}},} \end{matrix}\quad} & (34) \end{matrix}$

[0102] where r:=F_(J) ^(H) {tilde over (D)} ₁₂z. We let r_(n):=[r]_(n), and s_(n):=[s]_(n). Recognizing that {tilde over (H)}_(μ)s expresses nothing but a circular convolution between the channel h and s, we have [{tilde over (H)}_(μ)s]_(n)=Σ₂₀ ^(L)h_(μ)(l)s_((n−1) mod f). Hence, we obtain: $\begin{matrix} {{\hat{s}}_{M\quad L} = {a\quad r\quad g\quad \max {\sum\limits_{n = 0}^{J - 1}{\left\{ {\frac{1}{N_{0}}\left\lbrack \left. {{2R\quad e\left\{ {s_{n}^{*}r_{n}} \right\}} - \sum\limits_{\mu = 1}^{2}} \middle| {\sum\limits_{l = 0}^{L}{{h_{\mu}(l)}s_{{({n - l})}m\quad o\quad d\quad J}}} \right|^{2} \right\rbrack}\quad \right\}.}}}} & (35) \end{matrix}$

[0103] For each n=0, 1, . . . , J, let us define a sequence of state vectors as: ζ_(n)=[s_((n−1)mod J), . . . , s_((n−L)mod J)]^(T), out of which the first and the last states are known: ζ₀=ζ_(J)=[s_(J−1), . . . , s_(J−L)]^(T). The symbol sequence s₀, . . . , s_(J−1) determines an unique path evolving from the known initial state ζ₀ to the know final state ζ_(J). Thus, Viterbi's algorithm is applicable Specifically, we have: $\begin{matrix} {{{\hat{s}}_{ML} = {\arg \quad \max {\sum\limits_{n = 0}^{J - 1}{f\left( {\zeta_{n},\zeta_{n + 1}} \right)}}}},} & (36) \end{matrix}$

[0104] where ∫(ζ_(n2)ζ_(n+1)) is the branch metric, that is readily obtainable from (35). The explicit recursion formula for Viterbi's Algorithm is well known.

[0105] We now simplify the branch metric further. We first have Σ_(μ21) ²∥{overscore (H)}_(μ)s∥²=s^(H)Σ_(μ21) ²({overscore (H)}_(μ) ^(H){overscore (H)}_(μ))s. The matrix {overscore (H)}:=Σ_(μ21) ²({overscore (H)}_(μ) ^(H){overscore (H)}_(μ)) has (p,q)th entry: $\begin{matrix} {\left| \overset{\_}{H} \right|_{p,q} = {\sum\limits_{\mu = 1}^{2}{\sum\limits_{n = 0}^{J - 1}{{h_{\mu}^{*}\left( {\left( {k - p} \right){mod}\quad J} \right)}{{h_{\mu}\left( {\left( {k - q} \right){{mod}J}} \right)}.}}}}} & (37) \end{matrix}$

[0106] Let us now select J>2L an define $\begin{matrix} {{\beta_{n} = {\sum\limits_{\mu = 1}^{2}{\sum\limits_{l - 0}^{L}{{h_{\mu}^{*}(l)}{h_{\mu}\left( {n + l} \right)}}}}},{{{fo}\quad r\quad n} = 0},1,\ldots,{L.}} & (38) \end{matrix}$

[0107] It can be easily verified that the first column of {overscore (H)} is [β₀, β₁, . . . , β_(L), 0, . . . , 0, β_(L)*, . . . , β₁*]^(T). Let {tilde over (H)} denote the circulant matrix with first column [(½)β₀, β₁, . . . , β_(L), 0, . . . , 0]^(T). Because {overscore (H)} is circulant and Her-mitian {overscore (H)} can be decomposed into: {overscore (H)}={tilde over (H)}+{tilde over (H)}^(H). We thus obtain s^(H){overscore (H)}s=2Re{s^(H){tilde over (H)}s}. Recognizing [{tilde over (H)}s]_(n)=(½)β₀s_(n)+Σ_(l=1) ^(L)β_(l)s_((n−l)mod J, and combining with ()35) we obtain a simplified metric as: $\begin{matrix} {{f\left( {\zeta_{n},\zeta_{n + 1}} \right)} = {\frac{2}{N_{0}}R\quad e{\left\{ {s_{n}^{*}\left\lbrack {r_{n} - {\frac{1}{2}\beta_{0}s_{n}} - {\sum\limits_{l = 1}^{L}{\beta_{1}s_{{({n - 1})}{modJ}}}}} \right\rbrack}\quad \right\}.}}} & (39) \end{matrix}$

[0108] The branch metric in (39) has a format analogous to the one proposed by Ungerboeck for maximum-likelihood sequence estimation (MLSE) receivers with single antenna serial transmissions. For multi-antenna block coded transmissions, a similar metric has been suggested in conventional systems. The systems, however, can suffer from “edge effects” for transmissions with finite block length, resulting an approximation on the order of O (L/J), while our derivation here is exact. Our CP based design assures a circular convolution, while the linear convolution in some conventional systems approximates well a circulant convolution only when J>>L. Note also that we allow for an arbitrary permutation matrix P, which includes the time-reversal in as a special case. Furthermore, a known symbol vector b can be placed in an arbitrary position within the vector s for AP-CP-only. If the known symbols occupy positions B−L, . . . , B−1, we just need to redefine the states as

ζ_(n) =[s _((n+B−1)mod J) , . . . , s _((n+B−L)mod J)]^(T).

[0109] Notice that for channels with order L, the complexity of Viterbi's algorithm is O (|A|^(L)) per symbol; thus, ML decoding with our exact application of Viterbi's algorithm should be particularly attractive for transmissions with small constellation size, over relatively short channels.

[0110] B. Turbo Equalization for Coded AP-CP-Only and ZP-Only

[0111] So far, we have only considered uncoded systems, and established that full diversity is achieved. To further improve system performance by enhancing also coding gains, conventional channel coding can be applied to our systems. For example, outer convolutional codes can be used in AP-CP-only and ZP-only, as depicted in FIG. 5. Other codes such as TCM and turbo codes are applicable as well.

[0112] In the presence of frequency selective channels, iterative (turbo) equalization is known to enhance system performance, at least for single antenna transmissions. We here derive turbo equalizers for our coded AP-CP-only and ZP-only multi-antenna systems.

[0113] To enable turbo equalizations one needs to find the a posteriori probability on the transmitted symbols s_(m) based on the received vector z. Suppose each constellation point s_(n) is determined by Q=log₂|A| bits {c_(n.0), . . . , c_(n, Q−1)}. Let us consider the log likelihood ratio (LLR): $\begin{matrix} {{L_{n,q} = {\ln \frac{P\left( {c_{n,q} = \left. {+ 1} \middle| z \right.} \right)}{P\left( {c_{n,q} = \left. {- 1} \middle| z \right.} \right)}}},{\forall{n \in \left\lbrack {0,{J - 1}} \right\rbrack}},{q \in {\left\lbrack {0,{Q - 1}} \right\rbrack.}}} & (40) \end{matrix}$

[0114] The log-likelihood ratio in (40) can be obtained by running two generalized Viterbi recursions: one in the forward direction and one in the backward direction period. Our branch metric is modified as follows:

g(ζ_(n), ζ_(n+1))=∫(ζ_(n), ζ_(n+1))+1nP(ζ_(n+1)|ζ_(n)).   (41)

[0115] This modification is needed to take into account the a priori probability P(ζ_(n+1)|ζ_(n)), determined by the extrinsic information from the convolutional channel decoders during the turbo iteration. When the transition from ζ_(n) to ζ_(n+1) is caused by the input symbol s_(n), we have 1nP(ζ_(n+1)|ζ_(n))=1nP(s_(n)). We assume that the bit interleaver in FIG. 5 renders the symbols s_(n) independent and equal likely, such that 1nP(s_(n))=Σ_(q=0) ^(Q−1) 1nP(c_(n,q)), which in turn can be determined by the LLRs for bits {c_(c,q)}_(q=0) ^(Q−1).

[0116] Finally, we remark that one could also adopt the known turbo decoding algorithm that is based on MMSE equalizers. This iterative receiver is applicable not only to AP-CP-only and ZP-only, but also to CP-only and LP-CP-only systems.

[0117] C. Receiver Complexity

[0118] Omitting the complexity of permutation and diagonal matrix multiplication, the linear processing to reach (13 only requires one size-J FFT per block, which amounts to O(log₂ J) per information symbol.

[0119] Channel equalization is then performed based on (13) for each block. We notice that the complexity is the same as the equalization complexity for single antenna block transmissions over FIR channels [43]. We refer the readers to [43] for detailed complexity comparisons of the different equalization options. For coded AP-CP-only and ZP-only, the complexity of turbo equalization is again the same as that of single antenna transmissions [13].

[0120] In summary, the overall receiver complexity for the two transmit antenna case is comparable to that of single antenna transmissions, with only one additional FFT per data block. This nice property originates from the orthogonal space-time block code design, that enables linear ML processing to collect antenna diversity. Depending desirable/affordable diversity-complexity tradeoffs, the designer is then provided with the flexibility to collect extra multipath-diversity gains.

[0121] III. Extension to Multiple Antennas

[0122] In Section I, we focused on N_(l)=2 transmit- and N_(r)=1 receive-antennae, In this section, we will extend our system design to the general case with N_(l)>'2 and/or N_(r)>1 antennas; For each μ=1, . . . , N_(l) and v=1, . . . , N_(r), we denote the channel between the μth transmit- and the vth receive-antennae as h_(μv):=[h_(μv)(0), . . . , h_(μv)(L)]^(T), and as before we model it as a zero-means complex Gaussian vector with covariance matrix R₂. Correspondingly, we define the effective channel order {overscore (L)}_(μv):=rank {R_(2, μv)}−1, which for a sufficiently rich scattering environment becomes {overscore (L)}_(μv)=L.

[0123] Transmit diversity with N_(l)>2 has been addressed in for OFDM based multicarrier transmissions over FIR channels by applying the orthogonal ST block codes of on each OFDM subcarrier. Here, we extend the orthogonal designs to single carrier block transmissions over frequency selective channels.

[0124] We will review briefly generalized orthogonal designs to introduce notation, starting with the basic definitions given in the context of frequency-flat channels:

[0125] Definition 1: Define x:=[x₁, . . . , x_(N) ₂ ]^(T), and let g_(r)(x) be an N_(d)×N_(l) matrix with entries 0, ±x₁, . . . , ±x_(N) ₂ . If g_(r) ^(T)(x)g_(r)(x)=α(x₁ ²+ . . . +x_(N) ₂ ²)I_(N) ₁ with α positive, then g_(r)(x) is termed a generalized real orthogonal design (GROD) in variables x₁, . . . , x_(N) ₂ of size N_(d)×N_(l) and rate R=N₂/N_(d).

[0126] Definition 2: Define x:=[x₁, . . . , x_(N) ₂ ]^(T), and let g_(c)(x) be an N_(d)×N₁ matrix with entries 0, ±x₁, ±x₁ ², . . . , ±x_(N) ₂ . If g_(c) ^(H)(x)g_(c)(x)=α(|x₁|²+ . . . +|x_(N) ₂ |²)I_(N) ₂ , with α positive, then g_(c)(x) is termed a generalized real orthogonal design (GCOD) in variables x₁, . . . , x_(N) ₂ of size N_(d)×N_(l) and rate R=N_(μ)/N_(d).

[0127] Explicit construction of g_(r)(x) with R=1 was discussed in [34] where it was also proved that the highest rate for g_(c)(x) is ½ when N_(l)>4. When N_(l)=3,4, there exist some sporadic codes with rate R=¾. Although the orthogonal designs with R=¾ for N_(l)=3 ,4 have been incorporated for multicarrier transmissions, we will not consider them in our single carrier block transmissions here; we will only consider the R=½ GCOD designs primarily because GCOD g_(c)(x) of R=½ can be constructed using the following steps (N₂=4 for N_(l)=3, 4, while N₂=8 for N_(l)=5, 6, 7, 8, [34]):

[0128] s1) construct GROD g_(r)(x) of size N_(o)×N_(l) with R=1;

[0129] s2) replace the symbols x₁, . . . , x_(N) ₂ in g_(r)(x) by their conjugates x₁*, . . . , x_(N) ₂ * to arrive at g_(r)(x*);

[0130] s3) form g_(c)(x)=[g_(r) ^(T)(x), g_(r) ^(T)(x²)]^(T).

[0131] As will be clear soon, we are explicitly taking into account the fact that all symbols from the upper-part of g_(c)(x) are un-conjugated, while all symbols from the lower-part are conjugated. The rate loss can be as high as 50%, when N₁>2.

[0132] With N_(t)>2, the space-time mapper takes N₂ consecutive blocks to output the following N₂J×N_(d) space time coded matrix (N_(d)=2N₂) $\begin{matrix} {{\overset{\_}{S}(i)} = {{ɛ\left\{ {{s\left( {i\quad N_{s}} \right)},\ldots,{s\left( {{i\quad N_{s}} + N_{s} - 1} \right)}} \right\}} = {\begin{bmatrix} {{\overset{\_}{s}}_{i}\left( {i\quad N_{d}} \right)} & \cdots & {{\overset{\_}{s}}_{i}\left( {{i\quad N_{d}} + N_{d} - 1} \right)} \\ \vdots & ⋰ & \vdots \\ {{\overset{\_}{s}}_{N_{i}}\left( {i\quad N_{d}} \right)} & \cdots & {{\overset{\_}{s}}_{N_{i}}\left( {{i\quad N_{d}} + N_{d} - 1} \right)} \end{bmatrix}{\begin{matrix} \left. \rightarrow\quad {time} \right. \\ \left. \downarrow\quad {space} \right. \end{matrix}.}}}} & (42) \end{matrix}$

[0133] The design steps are summarized as follows:

[0134] d1) construct g_(c) of size 2N₂×N_(l) in the variables x₁, . . . , x_(N) ₂ , as in s1)-s3);

[0135] d2) Replace x₁, . . . , x_(N) ₂ in g_(c) ^(T) by s(iN₂), . . . , s(iN₂+N₂−1);

[0136] d3) Replace x₁*, . . . , x_(N) ₂ * in g_(r) ^(T) by Ps*(iN₂), . . . , Ps*(iN₂+N₂−1), where P is taken properly for different schemes as explained in Section I.

[0137] At each block transmission slot i, {overscore (s)}_(μ)(i) is forwarded to the μth antenna, and transmitted through the FIR channel after CP insertion. Each receive antenna processes blocks independently as follows: The receiver removes the CP, and collects N_(d)=2N₂ blocks x(iN_(d)), . . . , x(iN_(d)+N_(d)−1). Then FFT is performed on the first N₂ blocks x(iN_(d)), . . . , x(iN_(d)+N₂−1), while permutation and conjugation is applied to the last N_(s) blocks: Px*(iN_(d)+N₂), . . . , Px*(iN_(d)+N_(d)−1), followed by FFT processing. Coherently combining the FFT outputs as we did for the two antennae case to derive (13 ), we obtain on each antenna the equivalent output after the optimal linear processing:

z _(v)(i)={overscore (D)} _(v) F _(J) s(i)+η_(v)(i),   (43)

[0138] where {overscore (D)}_(v):=[Σ_(μ21) ^(2v1)D′_(μ,v)D_(μ,v)]^(½) and D _(μ,v):=diag({overscore (h)}_(μv))=diag(Vh_(μv)).

[0139] We next stack the z_(v)(i) blocks to form {overscore (z)}(i)=[z₁ ^(T)(i), . . . , z_(Nr) ^(T)(i)]^(T) (likewise for {overscore (η)}(i), and define B:=[{overscore (D)}₁, . . . , {overscore (D)}_(Nr)]^(T), to obtain: {overscore (z)}(i)=BF_(J)s(i)+{overscore (η)}(i). Defining {overscore (B)}:=[Σ_(μ=1) ^(N1)Σ_(v=1) ^(Nr)D_(μv)*D_(μv)]^(½, we have B) ^(H)B={overscore (B)}². Therefore, we can construct a matrix U_(b):=B{overscore (B)}⁻¹, which has orthonormal columns U_(b) ^(H)U_(b)=L_(J), and satisfies U_(b) ^(H)B={overscore (B)}. As U_(b) and B share range spaces, multiplying U_(b) ^(H) by {overscore (z)}(i) incurs no loss of optimality, and leads to the following equivalent block:

z(i):=U _(b) ^(H) {overscore (z)}(i)={overscore (B)}F _(J) s(i)+η(i),   (44)

[0140] where the noise η(i) is still white. Now the distance between z and z′, corresponding to two different symbol blocks d and d′, becomes: $\begin{matrix} {{d^{2}\left( {z,z^{\prime}} \right)} = {\sum\limits_{\mu = 1}^{N_{b}}{\sum\limits_{v = 1}^{N_{r}}{{{D_{e}V\quad h_{\mu \quad v}}}^{2}.}}}} & (45) \end{matrix}$

[0141] Comparing (45) with (15), the contribution now comes from N_(t)N_(r) multipath channels. Following the same steps as in Section I, the following result can be established:

[0142] Proposition 1: The maximum achievable diversity order is Σ_(μ21) ^(Nr)Σ₂₁ ^(Nr)({overscore (L)}_(μv)+1) with N_(t) transmit- and N_(r) receive-antennas, which equals N₁N_(r)(L+1) when the channel correlation has full rank.

[0143] 1. CP-only achieves multi-antenna diversity of order N_(l)N_(r);

[0144] 2. LP-CP-only achieves the maximum diversity gain through either non-redundant but constellation-dependent, or, redundant but constellation-independent precoding;

[0145] 3. Affine precoded CP-only and ZP-only achieve the maximum diversity gain irrespective of the underlying signal constellation.

[0146] The linear ML processing to reach (44) requires a total of N_(d)N_(r)=2N₂N_(r) FFTs corresponding to each space-time coded block of (42), which amounts to 2N_(r) FFTs per information block. Channel equalization based on (44) incurs identical complexity as in single antenna transmissions. For AP-CP-only and ZP-only, the ML estimate s_(ML)=arg max⁻(−∥z−{overscore (B)}Fs∥²/N₀) can be obtained via exact application of Viterbi's algorithm. Relative to the two antenna case detailed in Section II-A, we can basically use the same expression for the branch metric of (39), with two modifications, namely: r_(n)=[r]_(n) with r=F_(J) ^(H){overscore (B)}z, and $\begin{matrix} {{\beta_{n} = {\sum\limits_{\mu = 1}^{N_{b}}{\sum\limits_{v = 1}^{N_{r}}{\sum\limits_{l - 0}^{L}{{h_{\mu \quad v}^{*}(l)}{h_{\mu \quad v}\left( {n + l} \right)}}}}}},{{{fo}\quad r\quad n} = 0},1,\ldots,{L.}} & (46) \end{matrix}$

[0147] We summarize the general complexity results of this section and those of Section II in the following.

[0148] Proposition 2: The proposed space-time block coded CP-only, LP-CP-only, AP-CP-only and ZP-only systems with N_(t)>2(N_(t)=2) transmit- and N_(r) receive-antennas require an additional complexity of O(2N_(r)log₂J) (respectively, O(N_(r)log₂J)) per information symbol, relative to their counterparts with single, transmit- and single receive-antenna, where J is the FFT size.

[0149] IV. Simulated Performance

[0150] In this section, we present simulation results for systems with two transmit- and one receive-antenna. For ease in FFT processing, we always choose the block size J to be a power of 2. In all figures, we define SNR as the average received symbol energy to noise ratio at the receive antenna. For reference, we also depict the (outage) probability that the channel capacity is less than the desired rate, so that reliable communication at this rate is impossible. Specifically, we calculate (28) numerically, we evaluate the outage probability at the targeted rate R as P(C_(J→x)<R) with Monte-Carlo simulations.

[0151] Test Case 1 (comparisons for different equalizers): We first set L=2, and assume that the channels between each transmit and each receive antenna are i.i.d., Gaussian, with covariance matrix I_(L+1)/(L+1). We investigate the performance of ZP-only with block sizes: K=14,and P=J=16. We adopt QPSK constellations. FIG. 6 depicts the block error rate performance corresponding to MMSE, DFE, SD, and ML equalizers. We observe that the SD equalizer indeed achieves near-ML performance, and outperforms the suboptimal block DFE as well as the block MMSE alternatives. Without channel coding, the performance of ZP-only is faraway from the outage probability at rate 2K/(K+L)=1.75 bits per channel use.

[0152] Test Case 2 (convolutionally coded ZP-only): We here use two i.i.d. taps per FIR channel, i.e., L=1. We set the block sizes as K=127, P=J=128 for our ZP-only system, and use 8-PSK constellation. For convenience, we view each block of length P=128 as one data frame, with the space time codes applied to two adjacent frames. Within each frame, the information bits are convolutionally coded (CC) with a 16-state rate ⅔ encoder. Omitting the trailing bits to terminate the CC trellis, and ignoring the rate loss induced by the CP since L<<K, we obtain a transmission rate of 2 bits per channel use.

[0153] Turbo decoding iterations are performed. With the 16-state convolutional code, the frame error rate for ZP-only is within 2.3 dB away from the outage probability.

[0154] Test Case 3 (convolutionally coded AP-CP-only over EDGE channels): We test the Typical Urban (TU) channel with a linearized GMSK transmit pulse shape, and a symbol duration T=3.69 μs as in the proposed third generation TDMA cellular standard EDGE (Enhance Date Rates for GSM Evolution). The channel has order L=3 and correlated taps. We use QPSK constellations, and set the block size J=128. We adopt AP-CP-only that guarantees perfectly constant modulus transmissions. Within each frame of 128 symbols, the last 3 are known. Information bits are coded using a 16-state rate ½ convolutional code. Taking into account the known symbols, the cyclic prefix, and zero bits to terminate the CC trellis, the overall transmission rate of the proposed AP-CP-only is (128−3−4)/(128+3)=0.924 bits per channel use, or 250.4 kbps.

[0155] As shown in FIG. 8, the system with two transmit antennas significantly outperforms its counterpart with one transmit antenna. At frame error rate of 10⁻², about 5 dB SNR gain has been achieved. FIG. 9 depicts the performance improvement with turbo iterations, which confirms the importance of iterative over non-iterative receivers. A large portion of the performance gain is achieved within three iterations.

[0156] Various embodiments of the invention have been described. These and other embodiments are within the scope of the following claims. 

1. A method comprising: parsing a stream of information-bearing symbols to form blocks of K symbols; preceding the symbols to form blocks having J symbols; collecting consecutive N_(s) blocks; applying a permutation matrix to the N_(s) blocks; generating a space-time block coded matrix having N_(t) rows, each row containing N_(D)*J symbols; generating N_(t) transmission signals from the symbols of the N_(t) rows; and within N_(D) block transmission time intervals, communicating the N_(t) transmission signals through N_(t) transmit antennas over a wireless communication medium.
 2. The method of claim 1, wherein J>K.
 3. The method of claim 1, wherein J=K.
 4. The method of claim 1, wherein N_(t)=2 and N_(D)=2, and applying the permutation matrix comprises applying the permutation matrix to generate the space-time coded matrix according to the following equation: $\begin{bmatrix} {s\left( {2i} \right)} & {{- P}\quad {s^{*}\left( {{2i} + 1} \right)}} \\ {s\left( {{2i} + 1} \right)} & {P\quad {s^{*}\left( {2i} \right)}} \end{bmatrix},$

where P represents a permutation matrix, i represents an index into the blocks of symbols, and s represents symbol block.
 5. The method of claim 4, wherein the permutation matrix is drawn from a set of permutation matrices {P_(J) ^((n))}_(n=0) ^(J−1).
 6. The method of claim 4, wherein each row of a second column of the space-time block coded matrix stores a block that is a conjugated and permuted version of a corresponding block from an other row of a first column.
 7. The method of claim 1, wherein preceding the symbols comprises adding a set of known symbols to each group of K symbols.
 8. The method of claim 7, wherein the set of known symbols comprises a preamble and a post amble.
 9. The method of claim 1, further comprising: receiving a signal from the wireless communication medium, wherein the signal comprises a stream of received symbols; parsing the received symbols of the input signal to form blocks of J symbols; applying the permutation matrix to the blocks of the received symbols; and separately demodulating transmitted data from the permutated blocks of received symbols.
 10. The method of claim 9, further comprising conjugating and applying a Fast Fourier Transform (FFT) to the blocks.
 11. An apparatus comprising: an encoder to apply a permutation matrix to blocks of information bearing symbols and to generate a space-time block coded matrix of the permutated blocks of symbols; a plurality of pulse shaping units to generate a plurality of transmission signals from the symbols of the space-time block coded matrix; and a plurality of antennae to communicate the transmission signals through a wireless communication medium.
 12. The apparatus of claim 11, wherein the encoder collects consecutive N_(s) blocks within a buffer, applies a permutation matrix to the N_(s) blocks, and forms a space-time block coded matrix having N_(t) rows of symbols.
 13. The apparatus of claim 11, further comprising a precoder to precode the symbols to form blocks having J symbols, wherein each row of the space-time block coded matrix contains N_(D)*J symbols, wherein N_(D) represents a number of block transmission time intervals for transmitting the space-time matrix.
 14. The apparatus of claim 11, wherein the precoder adds a set of known symbols to each group of K symbols.
 15. The apparatus of claim 14, wherein the set of known symbols comprises a preamble and a post amble.
 16. The apparatus of claim 11, wherein J>K.
 17. The apparatus of claim 11, wherein J=K.
 18. The apparatus of claim 11, wherein N_(t)=2 and the encoder applies the permutation matrix to generate the space-time coded matrix according to the following equation: $\begin{bmatrix} {s\left( {2i} \right)} & {{- P}\quad {s^{*}\left( {{2i} + 1} \right)}} \\ {s\left( {{2i} + 1} \right)} & {P\quad {s^{*}\left( {2i} \right)}} \end{bmatrix},$

where P represents a permutation matrix, i represents an index into the blocks of symbols, and s represents symbol block.
 19. The apparatus of claim 13, wherein the permutation matrix is drawn from a set of permutation matrices {P_(J) ^((n))}_(n=0) ^(J−1).
 20. The apparatus of claim 11, wherein the apparatus comprises a base station within a wireless communication system.
 21. The apparatus of claim 11, wherein the apparatus comprises one of a cellular phone, a personal digital assistant, a laptop computer, a desktop computer, a two-way communication device.
 22. A method comprising: applying a permutation matrix to blocks of symbols of an outbound data stream; generating transmission signals from the permutated blocks of symbols; and communicating the transmission signals through a wireless communication medium.
 23. The method of claim 22, further comprising generating a space-time block coded matrix having N_(t) rows, wherein N_(t) represents a number of transmitters within a transmission device.
 24. The method of claim 23, further comprising: parsing the outbound data stream of information-bearing symbols to form blocks of K symbols; precoding the symbols to form blocks having J symbols; collecting consecutive N_(s) blocks; and generating the space-time block coded matrix to have N_(t) rows and N_(D)*J symbols per row, wherein N_(D) represents a number of transmission time intervals for communicating the transmission signals. 